causalStudio™

Engineered to answer research questions that no other analytical software platform can tackle

Easy-to-Use, Modern Causal Inference Methods to Answer Key Business Questions

Target RWE’s proprietary causalStudio™ platform addresses real-world challenges by providing a software solution that leverages validated analytics proven and trusted for regulatory decision-making. causalStudio™ is equipped with advanced epidemiological algorithms and innovative statistical methods that produce both accurate and reliable insights. Our platform provides the tools to address bias often found in real-world analyses by accounting for confounding variables.

With the ability to deploy on any system and with any type of data, our causalStudio™ platform includes two offerings that enhance your data-driven decision-making: causalRisk™ and causalPHR™. 

causalRisk™

Validated and utilized to support regulatory decision-making, causalRisk™ simplifies the complex process of estimating the cumulative risk of outcomes, even in the presence of dependent censoring, missing data, confounding, selection bias, and competing risks.

  • Convenient R-based analytical packages designed for researchers and data analysts

  • Proprietary R packages that address complications in real-world data and can handle informative censoring, time-varying treatments, or time-varying covariates

  • Ability to conduct principled safety and effectiveness research using robust causal inference methods

Additional causalRisk™​ Capabilities Include

  • Range of helper functions to produce common plots and tables

  • Assessing the balance of confounders in an IPTW analysis

  • Handling multiple censoring variables

  • Estimating risk with more than two treatment groups

  • Summarizing weights

  • Propensity score distribution and trimming and truncation

  • Overlap, normalized, and stabilized weights

  • Bootstrap estimation

  • Estimation of cumulative count

  • Example data, vignettes, simulation functions, and testing/simulation scripts in a detailed user manual

causalPHR™

Our causalPHR™ data visualization tool seamlessly connects with causalRisk™ to make visualizing, stratifying, and filtering analytical results straightforward and intuitive.

  • Integrates scientific content relevant across product lifecycle phases into an interactive report, with powerful filtering and stratification capabilities to visualize real-world data

  • Ability to easily view trends in forecasting and treatment pattern analyses to high-risk patient subgroup identification and policy monitoring

  • Drag-and-drop visualizations tailored for the life sciences industry, including cumulative risk plots, forest plots, maps, line charts, Sankey, and Sunburst diagrams, and more

  • Full peer review collaborative workflow, with a table of contents, commenting, and extensive methods section to build your research story

Real-World Case Studies with causalStudio™

Negative Control Outcome Studies to Measure Confounding

Using Negative Control Outcomes to Assess the Comparability of Treatment Groups

Use of Negative Control Outcomes to Assess the Comparability of Patients Initiating Lipid-Lowering Therapies

Descriptive Epidemiology

Lipid Testing Trends Before and After Hospitalization for Myocardial Infarction Among Adults in the United States, 2008-2019

Treatment Adherence

Initiation and Interruption in Intravenous Bisphosphonate Therapy Among Patients with Multiple Myeloma in the United States

Estimating the Effect of Preventable Treatment Discontinuation on Health Outcomes

External Control Arm Studies

Approaches to Selecting "Time Zero" in External Control Arms with Multiple Potential Entry Points: A Simulation Study of 8 Approaches

External Comparator Groups Derived from Real-World Data Used in Support of Regulatory Decision Making: Use Cases and Challenges

Methodology Guidance

Using Instrumental Variables to Address Bias From Unobserved Confounders

Trial Emulation to Support Label Expansion

Lessons Learned Using Real-World Data to Emulate Randomized Trials: A Case Study of Treatment Effectiveness for Newly Diagnosed Immune Thrombocytopenia

Why causalStudio™?

causalStudio™ helps researchers navigate the complexities of real-world data to unlock cause-and-effect relationships that drive impactful decisions. Confidently run complex analyses and uncover causal relationships that drive informed decision-making that achieves precise and reliable outcomes.

Leading Technology: Apply data analyses and unlock rich insights with our leading analytical software platform developed by epidemiologists and statisticians for researchers

Regulatory Validation: Trust in analytical software that has been used and validated for regulatory decision-making

Comprehensive Solutions: Use tools that address the challenges of real-world data and identify meaningful insights

Flexible Deployment & Data Agnostic: Easily integrate our software platform on any system and utilize with any data source

Continuous Updates: Keep all study insights in one convenient location, with versioning and iterative updating functionality

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